1. Field of the Invention
The present invention generally relates to active control of periodic noise. In particular, it relates to noninvasive on-line secondary path modeling for the filtered-X LMS algorithm.
2. Description of the Related Art
In an effort to overcome problems unsolvable in the practical sense using passive techniques, typically the attenuation of undesired low frequency noise, active noise control has received considerable interest in recent decades. The physical principles involved in the control of sound by active techniques had long been established and understood. However, it was not until the 1980's that the advances in signal processing, actuator, and sensor technology allowed for extensive research and practical implementation of these techniques. The history of active noise and vibration control reveals that both interest and progress in this area have been closely associated with the development of relevant technology. This is attributed to the accurate amplitude and phase matching that is required of an active control system to produce high degrees of attenuation of the unwanted noise or vibration. Active noise control had become one of the most popular research topics in engineering with hundreds of journal papers covering dozens of associated topics reaching the academic press each year.
Despite the vast research effort, commercial success in applying active control technology, apart from active headsets and the occasional HVAC and aircraft implementations, has been extremely limited. This failure to “take off” has caused interest in active noise control to wane in recent years. The inability of controllers to guarantee performance and stability in all operating conditions renders the current active noise and vibration control technology essentially stillborn, preventing the technology from achieving broad applicability. Part of the difficulty has been in developing algorithms that perform robustly in applications where signals and environments are continuously changing. The ideal noise control system can track and cancel non-stationary signals and account for environmental changes simultaneously, all of this without the injection of any foreign noise into the system for the purpose of secondary path modeling, also known generally as system identification. However, the issue of stability and real-time adaptation has proven most arduous in achieving the goal of noise attenuation via a secondary source.
The filtered-X LMS algorithm is a modified form of the LMS algorithm for use when there is a non-unity transfer function in the secondary path following the adaptive filter which is the case for most practical ANC applications. In this formulation the reference signal is filtered by the secondary path estimate. Burgess coined the term “filtered-X” for the adaptive algorithm since the reference signal was often labeled x(t).
The filtered-X LMS algorithm requires an estimate of the secondary, or cancellation, path in order to compensate for any signal delays and gains associated with the path. This is done in essence to align the primary disturbance and the secondary disturbance at the error sensor in the eyes of the LMS algorithm. A sufficiently accurate model of this path is crucial to the success of the algorithm.
There are two basic approaches to this problem. One approach is the off-line approach where the path is modeled prior to active control system startup. The drawback to this approach is that the secondary path model can not evolve as the secondary path evolves due to such changes as temperature, humidity, air velocity (in a duct), all of which affects the sound speed and hence the delay in the path; the frequency response of system components tend to change due to temperature also, as well as age and operating conditions. If the plant can not track the changes in the system the performance of the algorithm suffers.
The other approach is to model the path on-line as the system is running. This would allow the secondary path model to track the secondary path as it evolves during system operation. This of course is favorable and necessary for the filtered-X LMS algorithm to work properly. There are basically two approaches for on-line secondary path modeling: invasive and noninvasive. The former involves the addition of what is often referred to as an interrogation signal; random noise or chirps of sufficient bandwidth are added to the system through the control source to determine directly the frequency response of this path. Besides the counterproductive appearance of injecting auxiliary noise for the purposes of noise control, this approach has been shown to suffer from misalignment in the secondary path estimates and long convergence times. Therefore the preferred approach is non-invasive wherein the control signals are utilized to perform system identification. However, this has proven to be non-trivial as this is still a topic of numerous papers published in active noise control.
No existing algorithm has been proven to provide consistently accurate secondary path models in a noninvasive fashion. Inaccurate estimates are the cause of instability in the application of the filtered-X LMS algorithm. The present invention addresses the challenge of highly accurate but still noninvasive on-line secondary path modeling.
Previous noninvasive algorithms have utilized iterative search methods that have proven unsuccessful in delivering sufficiently accurate secondary path models for use by the filtered-X LMS-based control system, especially in time-varying systems, resulting in poor performance and instability.